Overview

Dataset statistics

Number of variables37
Number of observations92
Missing cells67
Missing cells (%)2.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.7 KiB
Average record size in memory297.4 B

Variable types

Numeric24
DateTime3
Categorical10

Alerts

preciptype has constant value ""Constant
snow has constant value ""Constant
snowdepth has constant value ""Constant
severerisk has constant value ""Constant
source has constant value ""Constant
stations is highly imbalanced (89.1%)Imbalance
preciptype has 67 (72.8%) missing valuesMissing
Unnamed: 0 is uniformly distributedUniform
datetimeEpoch is uniformly distributedUniform
sunriseEpoch is uniformly distributedUniform
sunsetEpoch is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
datetime has unique valuesUnique
datetimeEpoch has unique valuesUnique
winddir has unique valuesUnique
sunriseEpoch has unique valuesUnique
sunsetEpoch has unique valuesUnique
Unnamed: 0 has 1 (1.1%) zerosZeros
tempmin has 1 (1.1%) zerosZeros
precip has 67 (72.8%) zerosZeros
precipcover has 67 (72.8%) zerosZeros
uvindex has 1 (1.1%) zerosZeros
moonphase has 3 (3.3%) zerosZeros

Reproduction

Analysis started2024-04-13 04:32:23.363839
Analysis finished2024-04-13 04:33:47.249157
Duration1 minute and 23.89 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIFORM  UNIQUE  ZEROS 

Distinct92
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.5
Minimum0
Maximum91
Zeros1
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size868.0 B
2024-04-13T00:33:47.383883image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.55
Q122.75
median45.5
Q368.25
95-th percentile86.45
Maximum91
Range91
Interquartile range (IQR)45.5

Descriptive statistics

Standard deviation26.70206
Coefficient of variation (CV)0.58685846
Kurtosis-1.2
Mean45.5
Median Absolute Deviation (MAD)23
Skewness0
Sum4186
Variance713
MonotonicityStrictly increasing
2024-04-13T00:33:47.576187image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
1.1%
58 1
 
1.1%
67 1
 
1.1%
66 1
 
1.1%
65 1
 
1.1%
64 1
 
1.1%
63 1
 
1.1%
62 1
 
1.1%
61 1
 
1.1%
60 1
 
1.1%
Other values (82) 82
89.1%
ValueCountFrequency (%)
0 1
1.1%
1 1
1.1%
2 1
1.1%
3 1
1.1%
4 1
1.1%
5 1
1.1%
6 1
1.1%
7 1
1.1%
8 1
1.1%
9 1
1.1%
ValueCountFrequency (%)
91 1
1.1%
90 1
1.1%
89 1
1.1%
88 1
1.1%
87 1
1.1%
86 1
1.1%
85 1
1.1%
84 1
1.1%
83 1
1.1%
82 1
1.1%

datetime
Date

UNIQUE 

Distinct92
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size868.0 B
Minimum2023-10-01 00:00:00
Maximum2023-12-31 00:00:00
2024-04-13T00:33:47.761948image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:48.022254image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

datetimeEpoch
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct92
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7000662 × 109
Minimum1.6961328 × 109
Maximum1.7039988 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size868.0 B
2024-04-13T00:33:48.384213image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1.6961328 × 109
5-th percentile1.6965259 × 109
Q11.6980984 × 109
median1.7000676 × 109
Q31.7020332 × 109
95-th percentile1.7036057 × 109
Maximum1.7039988 × 109
Range7866000
Interquartile range (IQR)3934800

Descriptive statistics

Standard deviation2308551.6
Coefficient of variation (CV)0.0013579186
Kurtosis-1.2006262
Mean1.7000662 × 109
Median Absolute Deviation (MAD)1989000
Skewness-0.00049533105
Sum1.5640609 × 1011
Variance5.3294103 × 1012
MonotonicityStrictly increasing
2024-04-13T00:33:48.690465image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1696132800 1
 
1.1%
1701147600 1
 
1.1%
1701925200 1
 
1.1%
1701838800 1
 
1.1%
1701752400 1
 
1.1%
1701666000 1
 
1.1%
1701579600 1
 
1.1%
1701493200 1
 
1.1%
1701406800 1
 
1.1%
1701320400 1
 
1.1%
Other values (82) 82
89.1%
ValueCountFrequency (%)
1696132800 1
1.1%
1696219200 1
1.1%
1696305600 1
1.1%
1696392000 1
1.1%
1696478400 1
1.1%
1696564800 1
1.1%
1696651200 1
1.1%
1696737600 1
1.1%
1696824000 1
1.1%
1696910400 1
1.1%
ValueCountFrequency (%)
1703998800 1
1.1%
1703912400 1
1.1%
1703826000 1
1.1%
1703739600 1
1.1%
1703653200 1
1.1%
1703566800 1
1.1%
1703480400 1
1.1%
1703394000 1
1.1%
1703307600 1
1.1%
1703221200 1
1.1%

tempmax
Real number (ℝ)

Distinct70
Distinct (%)76.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.518478
Minimum3.8
Maximum28.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size868.0 B
2024-04-13T00:33:48.906124image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile6.365
Q19.875
median14
Q318.375
95-th percentile26.6
Maximum28.2
Range24.4
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation5.9542566
Coefficient of variation (CV)0.41011575
Kurtosis-0.36064582
Mean14.518478
Median Absolute Deviation (MAD)4.25
Skewness0.44310692
Sum1335.7
Variance35.453171
MonotonicityNot monotonic
2024-04-13T00:33:49.093728image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.7 3
 
3.3%
11.7 2
 
2.2%
12.7 2
 
2.2%
13.3 2
 
2.2%
18.9 2
 
2.2%
6.6 2
 
2.2%
6.7 2
 
2.2%
19.2 2
 
2.2%
9.8 2
 
2.2%
19.3 2
 
2.2%
Other values (60) 71
77.2%
ValueCountFrequency (%)
3.8 1
1.1%
4.3 1
1.1%
4.9 1
1.1%
5.6 1
1.1%
6.2 1
1.1%
6.5 1
1.1%
6.6 2
2.2%
6.7 2
2.2%
7.7 2
2.2%
7.8 1
1.1%
ValueCountFrequency (%)
28.2 1
1.1%
27.8 2
2.2%
27 1
1.1%
26.6 2
2.2%
24.4 1
1.1%
24.3 1
1.1%
23.7 1
1.1%
23 1
1.1%
22.7 1
1.1%
22.1 1
1.1%

tempmin
Real number (ℝ)

ZEROS 

Distinct58
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0532609
Minimum-2.2
Maximum19.1
Zeros1
Zeros (%)1.1%
Negative2
Negative (%)2.2%
Memory size868.0 B
2024-04-13T00:33:49.279791image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-2.2
5-th percentile1.055
Q13.9
median7.8
Q311.125
95-th percentile17.145
Maximum19.1
Range21.3
Interquartile range (IQR)7.225

Descriptive statistics

Standard deviation5.0045573
Coefficient of variation (CV)0.62143241
Kurtosis-0.51647811
Mean8.0532609
Median Absolute Deviation (MAD)3.75
Skewness0.28124666
Sum740.9
Variance25.045594
MonotonicityNot monotonic
2024-04-13T00:33:49.538257image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.1 5
 
5.4%
7.8 4
 
4.3%
7.9 3
 
3.3%
11.7 3
 
3.3%
6.2 3
 
3.3%
6.1 3
 
3.3%
4.3 3
 
3.3%
1.1 3
 
3.3%
6.7 3
 
3.3%
3.8 3
 
3.3%
Other values (48) 59
64.1%
ValueCountFrequency (%)
-2.2 1
 
1.1%
-1.7 1
 
1.1%
0 1
 
1.1%
0.7 1
 
1.1%
1 1
 
1.1%
1.1 3
3.3%
1.6 1
 
1.1%
1.7 1
 
1.1%
2.1 2
2.2%
2.2 1
 
1.1%
ValueCountFrequency (%)
19.1 1
1.1%
18.9 1
1.1%
18.8 1
1.1%
17.9 1
1.1%
17.2 1
1.1%
17.1 1
1.1%
16.2 1
1.1%
16.1 1
1.1%
15.6 1
1.1%
15 1
1.1%

temp
Real number (ℝ)

Distinct74
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.982609
Minimum1
Maximum22.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size868.0 B
2024-04-13T00:33:49.738214image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.455
Q17.15
median10.2
Q314.275
95-th percentile20.745
Maximum22.3
Range21.3
Interquartile range (IQR)7.125

Descriptive statistics

Standard deviation5.2073618
Coefficient of variation (CV)0.47414617
Kurtosis-0.52692397
Mean10.982609
Median Absolute Deviation (MAD)3.65
Skewness0.32440616
Sum1010.4
Variance27.116617
MonotonicityNot monotonic
2024-04-13T00:33:49.930658image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.2 3
 
3.3%
13.7 3
 
3.3%
14.8 3
 
3.3%
9.8 2
 
2.2%
12.9 2
 
2.2%
7.5 2
 
2.2%
7 2
 
2.2%
11.7 2
 
2.2%
1 2
 
2.2%
7.3 2
 
2.2%
Other values (64) 69
75.0%
ValueCountFrequency (%)
1 2
2.2%
2.6 1
1.1%
2.8 1
1.1%
3.4 1
1.1%
3.5 1
1.1%
3.8 1
1.1%
3.9 1
1.1%
4.3 1
1.1%
4.4 2
2.2%
5 1
1.1%
ValueCountFrequency (%)
22.3 1
1.1%
21.8 1
1.1%
21.6 1
1.1%
21.5 1
1.1%
20.8 1
1.1%
20.7 1
1.1%
20.6 1
1.1%
20 1
1.1%
19.4 1
1.1%
18.2 1
1.1%

feelslikemax
Real number (ℝ)

Distinct72
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.116304
Minimum-0.2
Maximum28.7
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)1.1%
Memory size868.0 B
2024-04-13T00:33:50.136253image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-0.2
5-th percentile4.465
Q19.25
median14
Q318.375
95-th percentile26.6
Maximum28.7
Range28.9
Interquartile range (IQR)9.125

Descriptive statistics

Standard deviation6.5388489
Coefficient of variation (CV)0.46321252
Kurtosis-0.38634228
Mean14.116304
Median Absolute Deviation (MAD)4.6
Skewness0.19694234
Sum1298.7
Variance42.756544
MonotonicityNot monotonic
2024-04-13T00:33:50.363734image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.2 3
 
3.3%
15.7 3
 
3.3%
14.2 2
 
2.2%
19.8 2
 
2.2%
12.7 2
 
2.2%
13.3 2
 
2.2%
18.9 2
 
2.2%
11.7 2
 
2.2%
5 2
 
2.2%
19.2 2
 
2.2%
Other values (62) 70
76.1%
ValueCountFrequency (%)
-0.2 1
1.1%
2 1
1.1%
2.8 1
1.1%
4.2 1
1.1%
4.3 1
1.1%
4.6 1
1.1%
5 2
2.2%
5.4 1
1.1%
5.6 1
1.1%
5.7 1
1.1%
ValueCountFrequency (%)
28.7 1
1.1%
28.1 2
2.2%
27.1 1
1.1%
26.6 2
2.2%
24.4 1
1.1%
24.3 1
1.1%
23.7 1
1.1%
23 1
1.1%
22.7 1
1.1%
22.1 1
1.1%

feelslikemin
Real number (ℝ)

Distinct73
Distinct (%)79.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2195652
Minimum-7.1
Maximum19.1
Zeros0
Zeros (%)0.0%
Negative17
Negative (%)18.5%
Memory size868.0 B
2024-04-13T00:33:50.584525image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-7.1
5-th percentile-2.79
Q11
median5.7
Q311.125
95-th percentile17.145
Maximum19.1
Range26.2
Interquartile range (IQR)10.125

Descriptive statistics

Standard deviation6.3965348
Coefficient of variation (CV)1.0284537
Kurtosis-0.77861085
Mean6.2195652
Median Absolute Deviation (MAD)5.4
Skewness0.17091093
Sum572.2
Variance40.915657
MonotonicityNot monotonic
2024-04-13T00:33:50.788314image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.1 5
 
5.4%
1 3
 
3.3%
5.8 3
 
3.3%
11.7 3
 
3.3%
6 2
 
2.2%
3.9 2
 
2.2%
-1.1 2
 
2.2%
4.2 2
 
2.2%
6.1 2
 
2.2%
13.8 2
 
2.2%
Other values (63) 66
71.7%
ValueCountFrequency (%)
-7.1 1
1.1%
-6.7 1
1.1%
-4.9 1
1.1%
-3.2 1
1.1%
-2.9 1
1.1%
-2.7 1
1.1%
-2.3 1
1.1%
-1.9 1
1.1%
-1.8 1
1.1%
-1.6 1
1.1%
ValueCountFrequency (%)
19.1 1
1.1%
18.9 1
1.1%
18.8 1
1.1%
17.9 1
1.1%
17.2 1
1.1%
17.1 1
1.1%
16.2 1
1.1%
16.1 1
1.1%
15.6 1
1.1%
15 1
1.1%

feelslike
Real number (ℝ)

Distinct71
Distinct (%)77.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9913043
Minimum-3.9
Maximum22.4
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)4.3%
Memory size868.0 B
2024-04-13T00:33:50.990748image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-3.9
5-th percentile1.265
Q15.65
median9.45
Q314.125
95-th percentile20.745
Maximum22.4
Range26.3
Interquartile range (IQR)8.475

Descriptive statistics

Standard deviation6.1465196
Coefficient of variation (CV)0.61518691
Kurtosis-0.68181414
Mean9.9913043
Median Absolute Deviation (MAD)4.45
Skewness0.10594595
Sum919.2
Variance37.779704
MonotonicityNot monotonic
2024-04-13T00:33:51.251231image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 3
 
3.3%
13.9 3
 
3.3%
13.7 3
 
3.3%
9 3
 
3.3%
14.8 3
 
3.3%
12.2 2
 
2.2%
21.6 2
 
2.2%
11.4 2
 
2.2%
9.4 2
 
2.2%
5.5 2
 
2.2%
Other values (61) 67
72.8%
ValueCountFrequency (%)
-3.9 1
1.1%
-0.9 1
1.1%
-0.6 1
1.1%
-0.5 1
1.1%
1.1 1
1.1%
1.4 2
2.2%
1.5 2
2.2%
1.8 1
1.1%
2.1 1
1.1%
2.3 1
1.1%
ValueCountFrequency (%)
22.4 1
1.1%
21.8 1
1.1%
21.6 2
2.2%
20.8 1
1.1%
20.7 1
1.1%
20.6 1
1.1%
20 1
1.1%
19.4 1
1.1%
18.2 1
1.1%
17.8 1
1.1%

dew
Real number (ℝ)

Distinct76
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4804348
Minimum-8.5
Maximum19.1
Zeros0
Zeros (%)0.0%
Negative24
Negative (%)26.1%
Memory size868.0 B
2024-04-13T00:33:51.434246image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-8.5
5-th percentile-5.37
Q1-0.5
median4.75
Q38.7
95-th percentile15.08
Maximum19.1
Range27.6
Interquartile range (IQR)9.2

Descriptive statistics

Standard deviation6.2831755
Coefficient of variation (CV)1.4023584
Kurtosis-0.6253685
Mean4.4804348
Median Absolute Deviation (MAD)4.3
Skewness0.032567619
Sum412.2
Variance39.478294
MonotonicityNot monotonic
2024-04-13T00:33:51.621116image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.9 3
 
3.3%
-0.5 2
 
2.2%
9 2
 
2.2%
1 2
 
2.2%
4.8 2
 
2.2%
5.6 2
 
2.2%
8.2 2
 
2.2%
-3.2 2
 
2.2%
9.8 2
 
2.2%
7.1 2
 
2.2%
Other values (66) 71
77.2%
ValueCountFrequency (%)
-8.5 1
1.1%
-7.6 1
1.1%
-7.1 1
1.1%
-6.2 1
1.1%
-5.7 1
1.1%
-5.1 1
1.1%
-4.6 1
1.1%
-4.1 1
1.1%
-4 1
1.1%
-3.6 1
1.1%
ValueCountFrequency (%)
19.1 1
1.1%
16.9 1
1.1%
15.6 1
1.1%
15.5 1
1.1%
15.3 1
1.1%
14.9 1
1.1%
14.7 1
1.1%
14.5 1
1.1%
14 1
1.1%
12.4 1
1.1%

humidity
Real number (ℝ)

Distinct80
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.891304
Minimum48
Maximum92.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size868.0 B
2024-04-13T00:33:51.816075image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile48.965
Q157.375
median64.5
Q372.675
95-th percentile88.635
Maximum92.2
Range44.2
Interquartile range (IQR)15.3

Descriptive statistics

Standard deviation11.950729
Coefficient of variation (CV)0.18137035
Kurtosis-0.59841333
Mean65.891304
Median Absolute Deviation (MAD)7.45
Skewness0.57865836
Sum6062
Variance142.81992
MonotonicityNot monotonic
2024-04-13T00:33:52.013536image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66 3
 
3.3%
67.4 2
 
2.2%
56.5 2
 
2.2%
58.6 2
 
2.2%
59.2 2
 
2.2%
48 2
 
2.2%
58.4 2
 
2.2%
57.8 2
 
2.2%
64.5 2
 
2.2%
55.8 2
 
2.2%
Other values (70) 71
77.2%
ValueCountFrequency (%)
48 2
2.2%
48.4 1
1.1%
48.6 1
1.1%
48.8 1
1.1%
49.1 1
1.1%
50.2 1
1.1%
50.9 1
1.1%
51.1 1
1.1%
51.7 1
1.1%
52 1
1.1%
ValueCountFrequency (%)
92.2 1
1.1%
91.4 1
1.1%
90.5 1
1.1%
89.8 1
1.1%
88.8 1
1.1%
88.5 1
1.1%
87.3 1
1.1%
85.9 1
1.1%
85.5 1
1.1%
85 1
1.1%

precip
Real number (ℝ)

ZEROS 

Distinct26
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20271739
Minimum0
Maximum3.124
Zeros67
Zeros (%)72.8%
Negative0
Negative (%)0.0%
Memory size868.0 B
2024-04-13T00:33:52.251771image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.0285
95-th percentile1.05705
Maximum3.124
Range3.124
Interquartile range (IQR)0.0285

Descriptive statistics

Standard deviation0.5507704
Coefficient of variation (CV)2.7169371
Kurtosis15.547771
Mean0.20271739
Median Absolute Deviation (MAD)0
Skewness3.7632716
Sum18.65
Variance0.30334803
MonotonicityNot monotonic
2024-04-13T00:33:52.440088image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 67
72.8%
0.204 1
 
1.1%
2.085 1
 
1.1%
0.298 1
 
1.1%
0.033 1
 
1.1%
3.124 1
 
1.1%
0.184 1
 
1.1%
0.873 1
 
1.1%
1.438 1
 
1.1%
0.904 1
 
1.1%
Other values (16) 16
 
17.4%
ValueCountFrequency (%)
0 67
72.8%
0.014 1
 
1.1%
0.028 1
 
1.1%
0.03 1
 
1.1%
0.033 1
 
1.1%
0.04 1
 
1.1%
0.093 1
 
1.1%
0.184 1
 
1.1%
0.195 1
 
1.1%
0.204 1
 
1.1%
ValueCountFrequency (%)
3.124 1
1.1%
2.945 1
1.1%
2.085 1
1.1%
1.438 1
1.1%
1.139 1
1.1%
0.99 1
1.1%
0.904 1
1.1%
0.873 1
1.1%
0.726 1
1.1%
0.672 1
1.1%

precipprob
Categorical

Distinct2
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size868.0 B
0.0
67 
100.0
25 

Length

Max length5
Median length3
Mean length3.5434783
Min length3

Characters and Unicode

Total characters326
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 67
72.8%
100.0 25
 
27.2%

Length

2024-04-13T00:33:52.653038image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T00:33:52.804864image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 67
72.8%
100.0 25
 
27.2%

Most occurring characters

ValueCountFrequency (%)
0 209
64.1%
. 92
28.2%
1 25
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 326
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 209
64.1%
. 92
28.2%
1 25
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 326
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 209
64.1%
. 92
28.2%
1 25
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 326
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 209
64.1%
. 92
28.2%
1 25
 
7.7%

precipcover
Real number (ℝ)

ZEROS 

Distinct15
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.9671739
Minimum0
Maximum70.83
Zeros67
Zeros (%)72.8%
Negative0
Negative (%)0.0%
Memory size868.0 B
2024-04-13T00:33:52.937107image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38.33
95-th percentile51.455
Maximum70.83
Range70.83
Interquartile range (IQR)8.33

Descriptive statistics

Standard deviation17.96563
Coefficient of variation (CV)2.0034885
Kurtosis3.0515526
Mean8.9671739
Median Absolute Deviation (MAD)0
Skewness2.0199559
Sum824.98
Variance322.76386
MonotonicityNot monotonic
2024-04-13T00:33:53.288283image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 67
72.8%
33.33 4
 
4.3%
8.33 3
 
3.3%
12.5 3
 
3.3%
41.67 2
 
2.2%
45.83 2
 
2.2%
58.33 2
 
2.2%
25 2
 
2.2%
16.67 1
 
1.1%
4.17 1
 
1.1%
Other values (5) 5
 
5.4%
ValueCountFrequency (%)
0 67
72.8%
4.17 1
 
1.1%
8.33 3
 
3.3%
12.5 3
 
3.3%
16.67 1
 
1.1%
25 2
 
2.2%
29.17 1
 
1.1%
33.33 4
 
4.3%
37.5 1
 
1.1%
41.67 2
 
2.2%
ValueCountFrequency (%)
70.83 1
 
1.1%
66.67 1
 
1.1%
62.5 1
 
1.1%
58.33 2
2.2%
45.83 2
2.2%
41.67 2
2.2%
37.5 1
 
1.1%
33.33 4
4.3%
29.17 1
 
1.1%
25 2
2.2%

preciptype
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)4.0%
Missing67
Missing (%)72.8%
Memory size868.0 B
['rain']
25 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters200
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row['rain']
2nd row['rain']
3rd row['rain']
4th row['rain']
5th row['rain']

Common Values

ValueCountFrequency (%)
['rain'] 25
 
27.2%
(Missing) 67
72.8%

Length

2024-04-13T00:33:53.467239image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T00:33:53.610299image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
rain 25
100.0%

Most occurring characters

ValueCountFrequency (%)
' 50
25.0%
[ 25
12.5%
r 25
12.5%
a 25
12.5%
i 25
12.5%
n 25
12.5%
] 25
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 50
25.0%
[ 25
12.5%
r 25
12.5%
a 25
12.5%
i 25
12.5%
n 25
12.5%
] 25
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 50
25.0%
[ 25
12.5%
r 25
12.5%
a 25
12.5%
i 25
12.5%
n 25
12.5%
] 25
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 50
25.0%
[ 25
12.5%
r 25
12.5%
a 25
12.5%
i 25
12.5%
n 25
12.5%
] 25
12.5%

snow
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size868.0 B
0.0
92 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters276
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 92
100.0%

Length

2024-04-13T00:33:53.768564image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T00:33:53.884719image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 92
100.0%

Most occurring characters

ValueCountFrequency (%)
0 184
66.7%
. 92
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 276
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 184
66.7%
. 92
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 276
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 184
66.7%
. 92
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 276
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 184
66.7%
. 92
33.3%

snowdepth
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size868.0 B
0.0
92 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters276
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 92
100.0%

Length

2024-04-13T00:33:54.014175image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T00:33:54.128427image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 92
100.0%

Most occurring characters

ValueCountFrequency (%)
0 184
66.7%
. 92
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 276
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 184
66.7%
. 92
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 276
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 184
66.7%
. 92
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 276
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 184
66.7%
. 92
33.3%

windgust
Real number (ℝ)

Distinct53
Distinct (%)57.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.779348
Minimum11.2
Maximum92.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size868.0 B
2024-04-13T00:33:54.284883image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum11.2
5-th percentile16.78
Q142.65
median60.15
Q370.275
95-th percentile78.545
Maximum92.5
Range81.3
Interquartile range (IQR)27.625

Descriptive statistics

Standard deviation18.99736
Coefficient of variation (CV)0.34058053
Kurtosis-0.38359777
Mean55.779348
Median Absolute Deviation (MAD)12.25
Skewness-0.60641712
Sum5131.7
Variance360.89968
MonotonicityNot monotonic
2024-04-13T00:33:54.566207image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68.4 7
 
7.6%
29.5 5
 
5.4%
72 4
 
4.3%
63 4
 
4.3%
55.4 4
 
4.3%
38.9 4
 
4.3%
64.8 3
 
3.3%
77.8 3
 
3.3%
70.2 3
 
3.3%
76 2
 
2.2%
Other values (43) 53
57.6%
ValueCountFrequency (%)
11.2 2
 
2.2%
13 1
 
1.1%
14.8 2
 
2.2%
18.4 1
 
1.1%
27.7 1
 
1.1%
29.5 5
5.4%
31.3 1
 
1.1%
31.7 1
 
1.1%
35.3 2
 
2.2%
36.4 1
 
1.1%
ValueCountFrequency (%)
92.5 1
 
1.1%
87.8 1
 
1.1%
81.4 1
 
1.1%
79.6 1
 
1.1%
78.6 1
 
1.1%
78.5 1
 
1.1%
77.8 3
3.3%
76.2 1
 
1.1%
76 2
2.2%
73.8 2
2.2%

windspeed
Real number (ℝ)

Distinct58
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.909783
Minimum10
Maximum37.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size868.0 B
2024-04-13T00:33:54.837813image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile11.01
Q114.5
median17.8
Q321.875
95-th percentile29.735
Maximum37.9
Range27.9
Interquartile range (IQR)7.375

Descriptive statistics

Standard deviation5.8137212
Coefficient of variation (CV)0.30744516
Kurtosis0.46957776
Mean18.909783
Median Absolute Deviation (MAD)3.3
Skewness0.89865479
Sum1739.7
Variance33.799354
MonotonicityNot monotonic
2024-04-13T00:33:55.024562image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.1 6
 
6.5%
17.8 5
 
5.4%
14.5 4
 
4.3%
14.7 3
 
3.3%
16.1 3
 
3.3%
16.3 3
 
3.3%
12.8 2
 
2.2%
19.9 2
 
2.2%
14.4 2
 
2.2%
31 2
 
2.2%
Other values (48) 60
65.2%
ValueCountFrequency (%)
10 1
1.1%
10.8 2
2.2%
10.9 2
2.2%
11.1 1
1.1%
11.2 1
1.1%
12.5 1
1.1%
12.6 1
1.1%
12.8 2
2.2%
13 1
1.1%
13.3 1
1.1%
ValueCountFrequency (%)
37.9 1
1.1%
32.7 1
1.1%
32.4 1
1.1%
31 2
2.2%
28.7 2
2.2%
28.5 1
1.1%
27.5 1
1.1%
27.4 1
1.1%
27.1 1
1.1%
26.8 1
1.1%

winddir
Real number (ℝ)

UNIQUE 

Distinct92
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean205.475
Minimum1.7
Maximum358.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size868.0 B
2024-04-13T00:33:55.223626image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1.7
5-th percentile23.29
Q167.725
median253.4
Q3272.4
95-th percentile341.125
Maximum358.2
Range356.5
Interquartile range (IQR)204.675

Descriptive statistics

Standard deviation108.04753
Coefficient of variation (CV)0.52584271
Kurtosis-1.035435
Mean205.475
Median Absolute Deviation (MAD)36.75
Skewness-0.7176093
Sum18903.7
Variance11674.269
MonotonicityNot monotonic
2024-04-13T00:33:55.416138image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
358.2 1
 
1.1%
271.5 1
 
1.1%
262.1 1
 
1.1%
9.5 1
 
1.1%
301 1
 
1.1%
265.1 1
 
1.1%
46.1 1
 
1.1%
265.6 1
 
1.1%
238.7 1
 
1.1%
252.4 1
 
1.1%
Other values (82) 82
89.1%
ValueCountFrequency (%)
1.7 1
1.1%
9.5 1
1.1%
13.8 1
1.1%
18.9 1
1.1%
22.3 1
1.1%
24.1 1
1.1%
28.7 1
1.1%
30.9 1
1.1%
39.1 1
1.1%
39.5 1
1.1%
ValueCountFrequency (%)
358.2 1
1.1%
351.5 1
1.1%
347.3 1
1.1%
345 1
1.1%
341.4 1
1.1%
340.9 1
1.1%
328 1
1.1%
314.8 1
1.1%
310.9 1
1.1%
307.3 1
1.1%

pressure
Real number (ℝ)

Distinct82
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1018.2185
Minimum992.9
Maximum1036.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size868.0 B
2024-04-13T00:33:55.619000image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum992.9
5-th percentile1004.64
Q11011.375
median1019.1
Q31025.425
95-th percentile1031.635
Maximum1036.7
Range43.8
Interquartile range (IQR)14.05

Descriptive statistics

Standard deviation9.1674403
Coefficient of variation (CV)0.0090034119
Kurtosis-0.21480212
Mean1018.2185
Median Absolute Deviation (MAD)7.1
Skewness-0.34599913
Sum93676.1
Variance84.041962
MonotonicityNot monotonic
2024-04-13T00:33:55.824476image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1005 3
 
3.3%
1021.7 2
 
2.2%
1012.2 2
 
2.2%
1030.1 2
 
2.2%
1022.1 2
 
2.2%
1016.5 2
 
2.2%
1019.6 2
 
2.2%
1009.6 2
 
2.2%
1026.4 2
 
2.2%
1011.4 1
 
1.1%
Other values (72) 72
78.3%
ValueCountFrequency (%)
992.9 1
 
1.1%
993.6 1
 
1.1%
1000.4 1
 
1.1%
1003.8 1
 
1.1%
1004.2 1
 
1.1%
1005 3
3.3%
1005.7 1
 
1.1%
1006.6 1
 
1.1%
1006.7 1
 
1.1%
1007 1
 
1.1%
ValueCountFrequency (%)
1036.7 1
1.1%
1032.2 1
1.1%
1032.1 1
1.1%
1031.9 1
1.1%
1031.8 1
1.1%
1031.5 1
1.1%
1031.2 1
1.1%
1031.1 1
1.1%
1030.4 1
1.1%
1030.1 2
2.2%

cloudcover
Real number (ℝ)

Distinct83
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.530435
Minimum0.3
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size868.0 B
2024-04-13T00:33:56.008204image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.7
Q16.675
median32
Q374.425
95-th percentile98.125
Maximum100
Range99.7
Interquartile range (IQR)67.75

Descriptive statistics

Standard deviation35.293774
Coefficient of variation (CV)0.87079683
Kurtosis-1.3612834
Mean40.530435
Median Absolute Deviation (MAD)30.35
Skewness0.39295173
Sum3728.8
Variance1245.6505
MonotonicityNot monotonic
2024-04-13T00:33:56.208010image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 3
 
3.3%
0.8 3
 
3.3%
1.1 2
 
2.2%
98.4 2
 
2.2%
6.8 2
 
2.2%
0.7 2
 
2.2%
0.4 2
 
2.2%
74.1 1
 
1.1%
17.5 1
 
1.1%
66.9 1
 
1.1%
Other values (73) 73
79.3%
ValueCountFrequency (%)
0.3 1
 
1.1%
0.4 2
2.2%
0.6 1
 
1.1%
0.7 2
2.2%
0.8 3
3.3%
0.9 1
 
1.1%
1 1
 
1.1%
1.1 2
2.2%
1.4 1
 
1.1%
1.5 1
 
1.1%
ValueCountFrequency (%)
100 3
3.3%
98.4 2
2.2%
97.9 1
 
1.1%
97.6 1
 
1.1%
96.7 1
 
1.1%
93.8 1
 
1.1%
93.2 1
 
1.1%
92.4 1
 
1.1%
91.5 1
 
1.1%
91.2 1
 
1.1%

visibility
Real number (ℝ)

Distinct27
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.211957
Minimum7.6
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size868.0 B
2024-04-13T00:33:56.392885image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum7.6
5-th percentile11.265
Q115.375
median15.9
Q316
95-th percentile16
Maximum16
Range8.4
Interquartile range (IQR)0.625

Descriptive statistics

Standard deviation1.6109408
Coefficient of variation (CV)0.10589964
Kurtosis7.1425847
Mean15.211957
Median Absolute Deviation (MAD)0.1
Skewness-2.6485877
Sum1399.5
Variance2.5951302
MonotonicityNot monotonic
2024-04-13T00:33:56.560019image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
16 42
45.7%
15.9 15
 
16.3%
15.8 4
 
4.3%
15.6 4
 
4.3%
15.7 3
 
3.3%
13.6 2
 
2.2%
15.3 2
 
2.2%
9.6 1
 
1.1%
10.9 1
 
1.1%
12.5 1
 
1.1%
Other values (17) 17
18.5%
ValueCountFrequency (%)
7.6 1
1.1%
9.6 1
1.1%
10.7 1
1.1%
10.9 1
1.1%
11.1 1
1.1%
11.4 1
1.1%
12.2 1
1.1%
12.5 1
1.1%
12.6 1
1.1%
12.7 1
1.1%
ValueCountFrequency (%)
16 42
45.7%
15.9 15
 
16.3%
15.8 4
 
4.3%
15.7 3
 
3.3%
15.6 4
 
4.3%
15.4 1
 
1.1%
15.3 2
 
2.2%
15.2 1
 
1.1%
15.1 1
 
1.1%
15 1
 
1.1%

solarradiation
Real number (ℝ)

Distinct88
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.172826
Minimum10.4
Maximum211
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size868.0 B
2024-04-13T00:33:56.736227image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum10.4
5-th percentile21.5
Q148.475
median96.25
Q3122.575
95-th percentile196.93
Maximum211
Range200.6
Interquartile range (IQR)74.1

Descriptive statistics

Standard deviation52.074005
Coefficient of variation (CV)0.54715203
Kurtosis-0.56483854
Mean95.172826
Median Absolute Deviation (MAD)39.7
Skewness0.35604122
Sum8755.9
Variance2711.702
MonotonicityNot monotonic
2024-04-13T00:33:56.938262image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
206.2 2
 
2.2%
101.3 2
 
2.2%
34.3 2
 
2.2%
37 2
 
2.2%
68.7 1
 
1.1%
85.6 1
 
1.1%
10.4 1
 
1.1%
77.5 1
 
1.1%
32.5 1
 
1.1%
102.2 1
 
1.1%
Other values (78) 78
84.8%
ValueCountFrequency (%)
10.4 1
1.1%
12.4 1
1.1%
15.3 1
1.1%
15.4 1
1.1%
18.2 1
1.1%
24.2 1
1.1%
24.8 1
1.1%
26.8 1
1.1%
28.5 1
1.1%
31.9 1
1.1%
ValueCountFrequency (%)
211 1
1.1%
206.2 2
2.2%
204.3 1
1.1%
201 1
1.1%
193.6 1
1.1%
192 1
1.1%
166.8 1
1.1%
164.5 1
1.1%
160.4 1
1.1%
159.7 1
1.1%

solarenergy
Real number (ℝ)

Distinct73
Distinct (%)79.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2282609
Minimum1
Maximum18.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size868.0 B
2024-04-13T00:33:57.139346image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.875
Q14.25
median8.4
Q310.55
95-th percentile16.915
Maximum18.2
Range17.2
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.4874998
Coefficient of variation (CV)0.54537646
Kurtosis-0.56289679
Mean8.2282609
Median Absolute Deviation (MAD)3.4
Skewness0.34974189
Sum757
Variance20.137654
MonotonicityNot monotonic
2024-04-13T00:33:57.351984image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.1 3
 
3.3%
10 3
 
3.3%
17.8 2
 
2.2%
6.8 2
 
2.2%
3 2
 
2.2%
8.4 2
 
2.2%
7.4 2
 
2.2%
3.4 2
 
2.2%
6.1 2
 
2.2%
8.8 2
 
2.2%
Other values (63) 70
76.1%
ValueCountFrequency (%)
1 1
1.1%
1.1 1
1.1%
1.2 1
1.1%
1.3 1
1.1%
1.6 1
1.1%
2.1 1
1.1%
2.2 1
1.1%
2.3 1
1.1%
2.5 1
1.1%
2.6 1
1.1%
ValueCountFrequency (%)
18.2 1
1.1%
17.8 2
2.2%
17.7 1
1.1%
17.3 1
1.1%
16.6 2
2.2%
14.4 1
1.1%
14.3 1
1.1%
13.8 1
1.1%
13.7 2
2.2%
13.5 1
1.1%

uvindex
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6630435
Minimum0
Maximum9
Zeros1
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size868.0 B
2024-04-13T00:33:57.550177image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q36
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0927776
Coefficient of variation (CV)0.44880079
Kurtosis-0.63703859
Mean4.6630435
Median Absolute Deviation (MAD)1
Skewness-0.12818602
Sum429
Variance4.3797181
MonotonicityNot monotonic
2024-04-13T00:33:57.696068image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 25
27.2%
6 17
18.5%
2 14
15.2%
7 9
 
9.8%
3 9
 
9.8%
4 5
 
5.4%
1 5
 
5.4%
8 4
 
4.3%
9 3
 
3.3%
0 1
 
1.1%
ValueCountFrequency (%)
0 1
 
1.1%
1 5
 
5.4%
2 14
15.2%
3 9
 
9.8%
4 5
 
5.4%
5 25
27.2%
6 17
18.5%
7 9
 
9.8%
8 4
 
4.3%
9 3
 
3.3%
ValueCountFrequency (%)
9 3
 
3.3%
8 4
 
4.3%
7 9
 
9.8%
6 17
18.5%
5 25
27.2%
4 5
 
5.4%
3 9
 
9.8%
2 14
15.2%
1 5
 
5.4%
0 1
 
1.1%

severerisk
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size868.0 B
10.0
92 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters368
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10.0
2nd row10.0
3rd row10.0
4th row10.0
5th row10.0

Common Values

ValueCountFrequency (%)
10.0 92
100.0%

Length

2024-04-13T00:33:57.841219image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T00:33:57.974454image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
10.0 92
100.0%

Most occurring characters

ValueCountFrequency (%)
0 184
50.0%
1 92
25.0%
. 92
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 368
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 184
50.0%
1 92
25.0%
. 92
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 368
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 184
50.0%
1 92
25.0%
. 92
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 368
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 184
50.0%
1 92
25.0%
. 92
25.0%
Distinct91
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size868.0 B
Minimum2024-04-13 06:30:49
Maximum2024-04-13 07:29:38
2024-04-13T00:33:58.113840image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:58.329337image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

sunriseEpoch
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct92
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7000917 × 109
Minimum1.6961575 × 109
Maximum1.7040252 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size868.0 B
2024-04-13T00:33:58.544266image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1.6961575 × 109
5-th percentile1.6965509 × 109
Q11.6981246 × 109
median1.7000918 × 109
Q31.7020589 × 109
95-th percentile1.703632 × 109
Maximum1.7040252 × 109
Range7867663
Interquartile range (IQR)3934254.2

Descriptive statistics

Standard deviation2308727.7
Coefficient of variation (CV)0.0013580019
Kurtosis-1.2001291
Mean1.7000917 × 109
Median Absolute Deviation (MAD)1988738.5
Skewness-0.00014683263
Sum1.5640843 × 1011
Variance5.3302234 × 1012
MonotonicityStrictly increasing
2024-04-13T00:33:58.763730image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1696157539 1
 
1.1%
1701172647 1
 
1.1%
1701950785 1
 
1.1%
1701864330 1
 
1.1%
1701777874 1
 
1.1%
1701691416 1
 
1.1%
1701604957 1
 
1.1%
1701518497 1
 
1.1%
1701432036 1
 
1.1%
1701345574 1
 
1.1%
Other values (82) 82
89.1%
ValueCountFrequency (%)
1696157539 1
1.1%
1696244000 1
1.1%
1696330461 1
1.1%
1696416922 1
1.1%
1696503384 1
1.1%
1696589846 1
1.1%
1696676308 1
1.1%
1696762771 1
1.1%
1696849234 1
1.1%
1696935697 1
1.1%
ValueCountFrequency (%)
1704025202 1
1.1%
1703938791 1
1.1%
1703852378 1
1.1%
1703765963 1
1.1%
1703679545 1
1.1%
1703593126 1
1.1%
1703506704 1
1.1%
1703420280 1
1.1%
1703333854 1
1.1%
1703247426 1
1.1%

sunset
Date

Distinct91
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size868.0 B
Minimum2024-04-13 16:28:24
Maximum2024-04-13 18:38:34
2024-04-13T00:33:58.980802image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:59.177311image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

sunsetEpoch
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct92
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7001281 × 109
Minimum1.6961999 × 109
Maximum1.7040587 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size868.0 B
2024-04-13T00:33:59.377166image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1.6961999 × 109
5-th percentile1.6965926 × 109
Q11.6981634 × 109
median1.7001275 × 109
Q31.7020925 × 109
95-th percentile1.7036654 × 109
Maximum1.7040587 × 109
Range7858775
Interquartile range (IQR)3929103.5

Descriptive statistics

Standard deviation2305911.7
Coefficient of variation (CV)0.0013563164
Kurtosis-1.199912
Mean1.7001281 × 109
Median Absolute Deviation (MAD)1986120.5
Skewness0.00067246874
Sum1.5641179 × 1011
Variance5.3172286 × 1012
MonotonicityStrictly increasing
2024-04-13T00:33:59.596268image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1696199914 1
 
1.1%
1701207016 1
 
1.1%
1701984505 1
 
1.1%
1701898108 1
 
1.1%
1701811713 1
 
1.1%
1701725321 1
 
1.1%
1701638931 1
 
1.1%
1701552544 1
 
1.1%
1701466158 1
 
1.1%
1701379775 1
 
1.1%
Other values (82) 82
89.1%
ValueCountFrequency (%)
1696199914 1
1.1%
1696286214 1
1.1%
1696372515 1
1.1%
1696458817 1
1.1%
1696545118 1
1.1%
1696631421 1
1.1%
1696717723 1
1.1%
1696804027 1
1.1%
1696890331 1
1.1%
1696976635 1
1.1%
ValueCountFrequency (%)
1704058689 1
1.1%
1703972242 1
1.1%
1703885796 1
1.1%
1703799352 1
1.1%
1703712910 1
1.1%
1703626470 1
1.1%
1703540032 1
1.1%
1703453596 1
1.1%
1703367161 1
1.1%
1703280729 1
1.1%

moonphase
Real number (ℝ)

ZEROS 

Distinct65
Distinct (%)70.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49902174
Minimum0
Maximum0.98
Zeros3
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size868.0 B
2024-04-13T00:33:59.812843image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.041
Q10.25
median0.525
Q30.735
95-th percentile0.93
Maximum0.98
Range0.98
Interquartile range (IQR)0.485

Descriptive statistics

Standard deviation0.28589962
Coefficient of variation (CV)0.57292017
Kurtosis-1.138167
Mean0.49902174
Median Absolute Deviation (MAD)0.235
Skewness-0.10755843
Sum45.91
Variance0.081738593
MonotonicityNot monotonic
2024-04-13T00:34:00.010698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.75 3
 
3.3%
0.5 3
 
3.3%
0.25 3
 
3.3%
0 3
 
3.3%
0.57 2
 
2.2%
0.03 2
 
2.2%
0.61 2
 
2.2%
0.62 2
 
2.2%
0.59 2
 
2.2%
0.42 2
 
2.2%
Other values (55) 68
73.9%
ValueCountFrequency (%)
0 3
3.3%
0.03 2
2.2%
0.05 1
 
1.1%
0.06 1
 
1.1%
0.07 1
 
1.1%
0.08 1
 
1.1%
0.1 2
2.2%
0.12 1
 
1.1%
0.13 1
 
1.1%
0.14 1
 
1.1%
ValueCountFrequency (%)
0.98 1
1.1%
0.96 2
2.2%
0.95 1
1.1%
0.93 2
2.2%
0.91 1
1.1%
0.9 2
2.2%
0.88 1
1.1%
0.87 1
1.1%
0.86 1
1.1%
0.85 1
1.1%

conditions
Categorical

Distinct5
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size868.0 B
Clear
36 
Partially cloudy
29 
Rain, Partially cloudy
14 
Rain, Overcast
11 
Overcast
 
2

Length

Max length22
Median length16
Mean length12.195652
Min length5

Characters and Unicode

Total characters1122
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClear
2nd rowClear
3rd rowClear
4th rowClear
5th rowClear

Common Values

ValueCountFrequency (%)
Clear 36
39.1%
Partially cloudy 29
31.5%
Rain, Partially cloudy 14
 
15.2%
Rain, Overcast 11
 
12.0%
Overcast 2
 
2.2%

Length

2024-04-13T00:34:00.201313image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T00:34:00.380752image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
partially 43
26.9%
cloudy 43
26.9%
clear 36
22.5%
rain 25
15.6%
overcast 13
 
8.1%

Most occurring characters

ValueCountFrequency (%)
l 165
14.7%
a 160
14.3%
r 92
 
8.2%
y 86
 
7.7%
i 68
 
6.1%
68
 
6.1%
c 56
 
5.0%
t 56
 
5.0%
e 49
 
4.4%
d 43
 
3.8%
Other values (10) 279
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1122
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 165
14.7%
a 160
14.3%
r 92
 
8.2%
y 86
 
7.7%
i 68
 
6.1%
68
 
6.1%
c 56
 
5.0%
t 56
 
5.0%
e 49
 
4.4%
d 43
 
3.8%
Other values (10) 279
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1122
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 165
14.7%
a 160
14.3%
r 92
 
8.2%
y 86
 
7.7%
i 68
 
6.1%
68
 
6.1%
c 56
 
5.0%
t 56
 
5.0%
e 49
 
4.4%
d 43
 
3.8%
Other values (10) 279
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1122
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 165
14.7%
a 160
14.3%
r 92
 
8.2%
y 86
 
7.7%
i 68
 
6.1%
68
 
6.1%
c 56
 
5.0%
t 56
 
5.0%
e 49
 
4.4%
d 43
 
3.8%
Other values (10) 279
24.9%

description
Categorical

Distinct16
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size868.0 B
Clear conditions throughout the day.
36 
Partly cloudy throughout the day.
20 
Cloudy skies throughout the day with rain.
Becoming cloudy in the afternoon.
Clearing in the afternoon.
Other values (11)
22 

Length

Max length74
Median length73
Mean length39.728261
Min length26

Characters and Unicode

Total characters3655
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)3.3%

Sample

1st rowClear conditions throughout the day.
2nd rowClear conditions throughout the day.
3rd rowClear conditions throughout the day.
4th rowClear conditions throughout the day.
5th rowClear conditions throughout the day.

Common Values

ValueCountFrequency (%)
Clear conditions throughout the day. 36
39.1%
Partly cloudy throughout the day. 20
21.7%
Cloudy skies throughout the day with rain. 5
 
5.4%
Becoming cloudy in the afternoon. 5
 
5.4%
Clearing in the afternoon. 4
 
4.3%
Cloudy skies throughout the day with a chance of rain throughout the day. 3
 
3.3%
Partly cloudy throughout the day with early morning rain. 3
 
3.3%
Partly cloudy throughout the day with rain. 3
 
3.3%
Partly cloudy throughout the day with a chance of rain throughout the day. 2
 
2.2%
Cloudy skies throughout the day with rain clearing later. 2
 
2.2%
Other values (6) 9
 
9.8%

Length

2024-04-13T00:34:00.564332image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 98
17.6%
throughout 85
15.3%
day 85
15.3%
cloudy 51
9.2%
clear 36
 
6.5%
conditions 36
 
6.5%
partly 31
 
5.6%
with 25
 
4.5%
rain 25
 
4.5%
skies 13
 
2.3%
Other values (11) 72
12.9%

Most occurring characters

ValueCountFrequency (%)
465
12.7%
t 378
 
10.3%
o 336
 
9.2%
h 298
 
8.2%
u 221
 
6.0%
a 219
 
6.0%
r 213
 
5.8%
e 190
 
5.2%
d 173
 
4.7%
y 170
 
4.7%
Other values (14) 992
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3655
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
465
12.7%
t 378
 
10.3%
o 336
 
9.2%
h 298
 
8.2%
u 221
 
6.0%
a 219
 
6.0%
r 213
 
5.8%
e 190
 
5.2%
d 173
 
4.7%
y 170
 
4.7%
Other values (14) 992
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3655
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
465
12.7%
t 378
 
10.3%
o 336
 
9.2%
h 298
 
8.2%
u 221
 
6.0%
a 219
 
6.0%
r 213
 
5.8%
e 190
 
5.2%
d 173
 
4.7%
y 170
 
4.7%
Other values (14) 992
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3655
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
465
12.7%
t 378
 
10.3%
o 336
 
9.2%
h 298
 
8.2%
u 221
 
6.0%
a 219
 
6.0%
r 213
 
5.8%
e 190
 
5.2%
d 173
 
4.7%
y 170
 
4.7%
Other values (14) 992
27.1%

icon
Categorical

Distinct4
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size868.0 B
clear-day
36 
partly-cloudy-day
29 
rain
25 
cloudy
 
2

Length

Max length17
Median length9
Mean length10.097826
Min length4

Characters and Unicode

Total characters929
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowclear-day
2nd rowclear-day
3rd rowclear-day
4th rowclear-day
5th rowclear-day

Common Values

ValueCountFrequency (%)
clear-day 36
39.1%
partly-cloudy-day 29
31.5%
rain 25
27.2%
cloudy 2
 
2.2%

Length

2024-04-13T00:34:00.732237image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T00:34:00.875008image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
clear-day 36
39.1%
partly-cloudy-day 29
31.5%
rain 25
27.2%
cloudy 2
 
2.2%

Most occurring characters

ValueCountFrequency (%)
a 155
16.7%
y 125
13.5%
l 96
10.3%
d 96
10.3%
- 94
10.1%
r 90
9.7%
c 67
7.2%
e 36
 
3.9%
o 31
 
3.3%
u 31
 
3.3%
Other values (4) 108
11.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 155
16.7%
y 125
13.5%
l 96
10.3%
d 96
10.3%
- 94
10.1%
r 90
9.7%
c 67
7.2%
e 36
 
3.9%
o 31
 
3.3%
u 31
 
3.3%
Other values (4) 108
11.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 155
16.7%
y 125
13.5%
l 96
10.3%
d 96
10.3%
- 94
10.1%
r 90
9.7%
c 67
7.2%
e 36
 
3.9%
o 31
 
3.3%
u 31
 
3.3%
Other values (4) 108
11.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 155
16.7%
y 125
13.5%
l 96
10.3%
d 96
10.3%
- 94
10.1%
r 90
9.7%
c 67
7.2%
e 36
 
3.9%
o 31
 
3.3%
u 31
 
3.3%
Other values (4) 108
11.6%

stations
Categorical

IMBALANCE 

Distinct3
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size868.0 B
['72505394728', '72055399999', 'KLGA', 'KJRB', 'F1417', 'KNYC', '72503014732']
90 
['72505394728', '72055399999', 'KLGA', 'KJRB', 'F8726', 'F1417', 'KNYC', '72503014732']
 
1
['72505394728', '72055399999', 'KLGA', 'KJRB', 'F8726', 'KNYC', 'F1417', '72503014732']
 
1

Length

Max length87
Median length78
Mean length78.195652
Min length78

Characters and Unicode

Total characters7194
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)2.2%

Sample

1st row['72505394728', '72055399999', 'KLGA', 'KJRB', 'F1417', 'KNYC', '72503014732']
2nd row['72505394728', '72055399999', 'KLGA', 'KJRB', 'F1417', 'KNYC', '72503014732']
3rd row['72505394728', '72055399999', 'KLGA', 'KJRB', 'F1417', 'KNYC', '72503014732']
4th row['72505394728', '72055399999', 'KLGA', 'KJRB', 'F1417', 'KNYC', '72503014732']
5th row['72505394728', '72055399999', 'KLGA', 'KJRB', 'F1417', 'KNYC', '72503014732']

Common Values

ValueCountFrequency (%)
['72505394728', '72055399999', 'KLGA', 'KJRB', 'F1417', 'KNYC', '72503014732'] 90
97.8%
['72505394728', '72055399999', 'KLGA', 'KJRB', 'F8726', 'F1417', 'KNYC', '72503014732'] 1
 
1.1%
['72505394728', '72055399999', 'KLGA', 'KJRB', 'F8726', 'KNYC', 'F1417', '72503014732'] 1
 
1.1%

Length

2024-04-13T00:34:01.018497image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T00:34:01.345829image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
72505394728 92
14.2%
72055399999 92
14.2%
klga 92
14.2%
kjrb 92
14.2%
f1417 92
14.2%
knyc 92
14.2%
72503014732 92
14.2%
f8726 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
' 1292
18.0%
7 554
 
7.7%
, 554
 
7.7%
554
 
7.7%
9 552
 
7.7%
2 462
 
6.4%
5 460
 
6.4%
0 368
 
5.1%
3 368
 
5.1%
1 276
 
3.8%
Other values (16) 1754
24.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7194
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 1292
18.0%
7 554
 
7.7%
, 554
 
7.7%
554
 
7.7%
9 552
 
7.7%
2 462
 
6.4%
5 460
 
6.4%
0 368
 
5.1%
3 368
 
5.1%
1 276
 
3.8%
Other values (16) 1754
24.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7194
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 1292
18.0%
7 554
 
7.7%
, 554
 
7.7%
554
 
7.7%
9 552
 
7.7%
2 462
 
6.4%
5 460
 
6.4%
0 368
 
5.1%
3 368
 
5.1%
1 276
 
3.8%
Other values (16) 1754
24.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7194
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 1292
18.0%
7 554
 
7.7%
, 554
 
7.7%
554
 
7.7%
9 552
 
7.7%
2 462
 
6.4%
5 460
 
6.4%
0 368
 
5.1%
3 368
 
5.1%
1 276
 
3.8%
Other values (16) 1754
24.4%

source
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size868.0 B
obs
92 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters276
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowobs
2nd rowobs
3rd rowobs
4th rowobs
5th rowobs

Common Values

ValueCountFrequency (%)
obs 92
100.0%

Length

2024-04-13T00:34:01.496420image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T00:34:01.604372image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
obs 92
100.0%

Most occurring characters

ValueCountFrequency (%)
o 92
33.3%
b 92
33.3%
s 92
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 276
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 92
33.3%
b 92
33.3%
s 92
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 276
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 92
33.3%
b 92
33.3%
s 92
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 276
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 92
33.3%
b 92
33.3%
s 92
33.3%

Interactions

2024-04-13T00:33:43.110220image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:24.031759image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:27.814731image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:31.309669image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:34.662246image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:37.704684image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:40.605036image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:43.788504image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:46.891048image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:50.181421image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:53.870250image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:57.475261image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:00.704626image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:08.200891image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:11.468240image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:14.561342image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:17.910364image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:21.394501image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:24.262587image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:27.514490image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:30.575948image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:33.708958image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:36.794477image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:39.878395image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:43.223622image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:24.152866image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:27.957325image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:31.423807image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:34.794008image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:37.801989image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:40.708609image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:43.917538image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:47.042676image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:50.310103image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:54.001023image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:57.595034image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:01.186249image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:08.337353image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:11.611406image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:14.667588image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:18.013963image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:21.502060image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:24.378768image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:27.638831image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:30.686180image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:33.811717image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:36.904429image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:39.994247image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:43.354032image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:24.293158image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:28.097571image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:31.558738image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:34.976066image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:37.928322image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:40.856180image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:44.084261image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:47.253581image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:50.451789image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:54.155895image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:57.732651image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:01.411942image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:08.509763image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:11.753782image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:14.810297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:18.166739image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:21.632291image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:24.509259image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:27.832340image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:30.839484image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:33.978358image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:37.062093image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:40.151194image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:43.465265image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:24.404203image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:28.214478image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:31.658859image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:35.122494image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:38.026369image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:40.986146image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:44.206888image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:47.394892image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:50.565865image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:54.273648image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:57.846946image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:01.594663image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:08.620195image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:11.859328image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:14.917224image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:18.324225image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:21.754652image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:24.606167image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:27.961891image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:30.952780image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:34.093043image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:37.184329image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:40.270194image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:43.570203image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:24.591607image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:28.340031image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:31.764281image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:35.237324image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:38.124692image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:41.107515image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:44.322742image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:47.552450image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:50.666500image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:54.431087image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:57.971536image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:01.877117image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2024-04-13T00:32:40.201326image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:43.294778image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:46.474209image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:49.743837image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:53.125997image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:57.007025image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:00.210424image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:07.698235image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:11.002307image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:14.116141image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:17.462568image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:21.016076image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:23.859473image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:27.061659image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:30.159747image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:33.292882image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:36.223599image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:39.462720image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:42.661410image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:45.746164image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:27.188319image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:31.025343image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:34.434206image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:37.466062image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:40.339207image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:43.491079image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:46.633238image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:49.876503image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:53.532259image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:57.177046image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:00.344751image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:07.876290image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:11.157218image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:14.265260image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:17.613302image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:21.149540image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:24.007722image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:27.245078image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:30.292614image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:33.439842image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:36.364014image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:39.617954image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:42.817931image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:46.081203image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:27.359503image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:31.174316image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:34.558032image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:37.599714image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:40.476922image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:43.656101image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:46.773374image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:50.009182image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:53.729393image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:32:57.337006image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:00.481414image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:08.049191image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:11.310643image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:14.438472image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:17.770232image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:21.286095image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:24.140306image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:27.409164image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:30.456328image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:33.597184image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:36.696113image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:39.762893image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-13T00:33:42.972063image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Missing values

2024-04-13T00:33:46.342040image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-13T00:33:47.006215image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0datetimedatetimeEpochtempmaxtempmintempfeelslikemaxfeelslikeminfeelslikedewhumidityprecipprecipprobprecipcoverpreciptypesnowsnowdepthwindgustwindspeedwinddirpressurecloudcovervisibilitysolarradiationsolarenergyuvindexsevererisksunrisesunriseEpochsunsetsunsetEpochmoonphaseconditionsdescriptioniconstationssource
002023-10-01169613280027.016.120.827.116.120.814.067.40.0000.00.00NaN0.00.029.512.8358.21021.71.116.0206.217.88.010.006:52:19169615753918:38:3416961999140.57ClearClear conditions throughout the day.clear-day['72505394728', '72055399999', 'KLGA', 'KJRB', 'F1417', 'KNYC', '72503014732']obs
112023-10-02169621920026.618.821.626.618.821.614.966.70.0000.00.00NaN0.00.064.812.818.91022.112.216.0211.018.28.010.006:53:20169624400018:36:5416962862140.61ClearClear conditions throughout the day.clear-day['72505394728', '72055399999', 'KLGA', 'KJRB', 'F1417', 'KNYC', '72503014732']obs
222023-10-03169630560027.817.121.528.117.121.615.368.50.0000.00.00NaN0.00.081.410.8275.81021.70.315.3206.217.87.010.006:54:21169633046118:35:1516963725150.64ClearClear conditions throughout the day.clear-day['72505394728', '72055399999', 'KLGA', 'KJRB', 'F1417', 'KNYC', '72503014732']obs
332023-10-04169639200028.218.922.328.718.922.416.972.40.0000.00.00NaN0.00.065.514.3222.51022.30.415.0204.317.77.010.006:55:22169641692218:33:3716964588170.67ClearClear conditions throughout the day.clear-day['72505394728', '72055399999', 'KLGA', 'KJRB', 'F1417', 'KNYC', '72503014732']obs
442023-10-05169647840024.417.220.724.417.220.715.673.50.0000.00.00NaN0.00.068.416.2152.21023.34.015.8201.017.37.010.006:56:24169650338418:31:5816965451180.70ClearClear conditions throughout the day.clear-day['72505394728', '72055399999', 'KLGA', 'KJRB', 'F1417', 'KNYC', '72503014732']obs
552023-10-06169656480022.119.120.622.119.120.619.191.40.204100.016.67['rain']0.00.029.514.572.11016.8100.010.741.43.63.010.006:57:26169658984618:30:2116966314210.75Rain, OvercastCloudy skies throughout the day with rain in the morning and afternoon.rain['72505394728', '72055399999', 'KLGA', 'KJRB', 'F1417', 'KNYC', '72503014732']obs
662023-10-07169665120020.713.318.220.713.318.215.584.90.508100.041.67['rain']0.00.049.116.130.91006.691.213.656.95.03.010.006:58:28169667630818:28:4316967177230.77Rain, OvercastCloudy skies throughout the day with a chance of rain throughout the day.rain['72505394728', '72055399999', 'KLGA', 'KJRB', 'F1417', 'KNYC', '72503014732']obs
772023-10-08169673760016.611.113.816.611.113.86.963.90.0000.00.00NaN0.00.038.928.5265.21005.033.616.0135.811.68.010.006:59:31169676277118:27:0716968040270.80Partially cloudyPartly cloudy throughout the day.partly-cloudy-day['72505394728', '72055399999', 'KLGA', 'KJRB', 'F1417', 'KNYC', '72503014732']obs
882023-10-09169682400017.110.613.717.110.613.75.357.60.0000.00.00NaN0.00.053.722.3259.11008.835.516.0160.413.89.010.007:00:34169684923418:25:3116968903310.83Partially cloudyPartly cloudy throughout the day.partly-cloudy-day['72505394728', '72055399999', 'KLGA', 'KJRB', 'F1417', 'KNYC', '72503014732']obs
992023-10-10169691040018.312.815.118.312.815.19.067.40.028100.04.17['rain']0.00.038.919.8250.01010.162.715.6147.812.77.010.007:01:37169693569718:23:5516969766350.87Rain, Partially cloudyPartly cloudy throughout the day with early morning rain.rain['72505394728', '72055399999', 'KLGA', 'KJRB', 'F8726', 'F1417', 'KNYC', '72503014732']obs
Unnamed: 0datetimedatetimeEpochtempmaxtempmintempfeelslikemaxfeelslikeminfeelslikedewhumidityprecipprecipprobprecipcoverpreciptypesnowsnowdepthwindgustwindspeedwinddirpressurecloudcovervisibilitysolarradiationsolarenergyuvindexsevererisksunrisesunriseEpochsunsetsunsetEpochmoonphaseconditionsdescriptioniconstationssource
82822023-12-2217032212004.9-2.21.02.8-6.7-0.9-8.550.20.0000.00.0NaN0.00.070.220.2340.91032.13.216.089.37.65.010.007:17:06170324742616:32:0917032807290.35ClearClear conditions throughout the day.clear-day['72505394728', '72055399999', 'KLGA', 'KJRB', 'F1417', 'KNYC', '72503014732']obs
83832023-12-2317033076006.72.14.46.1-0.52.3-3.159.20.0000.00.0NaN0.00.014.816.146.91032.272.416.037.53.12.010.007:17:34170333385416:32:4117033671610.38Partially cloudyPartly cloudy throughout the day.partly-cloudy-day['72505394728', '72055399999', 'KLGA', 'KJRB', 'F1417', 'KNYC', '72503014732']obs
84842023-12-2417033940007.95.66.67.22.75.83.781.80.033100.012.5['rain']0.00.011.214.0280.21030.198.415.934.33.02.010.007:18:00170342028016:33:1617034535960.42Rain, OvercastCloudy skies throughout the day with rain clearing later.rain['72505394728', '72055399999', 'KLGA', 'KJRB', 'F1417', 'KNYC', '72503014732']obs
85852023-12-2517034804008.96.27.58.83.36.15.687.30.0000.00.0NaN0.00.013.014.553.11030.488.212.634.33.02.010.007:18:24170350670416:33:5217035400320.45Partially cloudyPartly cloudy throughout the day.partly-cloudy-day['72505394728', '72055399999', 'KLGA', 'KJRB', 'F1417', 'KNYC', '72503014732']obs
86862023-12-2617035668008.56.77.48.43.96.35.788.80.0000.00.0NaN0.00.011.214.370.81026.491.57.637.03.22.010.007:18:46170359312616:34:3017036264700.50OvercastCloudy skies throughout the day.cloudy['72505394728', '72055399999', 'KLGA', 'KJRB', 'F1417', 'KNYC', '72503014732']obs
87872023-12-2717036532009.36.77.87.24.86.06.389.80.298100.025.0['rain']0.00.035.325.346.21018.297.612.528.52.52.010.007:19:05170367954516:35:1017037129100.52Rain, OvercastCloudy skies throughout the day with rain.rain['72505394728', '72055399999', 'KLGA', 'KJRB', 'F1417', 'KNYC', '72503014732']obs
88882023-12-28170373960011.78.910.111.75.39.08.690.52.085100.062.5['rain']0.00.047.137.939.51005.7100.010.915.41.31.010.007:19:23170376596316:35:5217037993520.55Rain, OvercastCloudy skies throughout the day with rain.rain['72505394728', '72055399999', 'KLGA', 'KJRB', 'F1417', 'KNYC', '72503014732']obs
89892023-12-29170382600012.27.99.512.26.88.96.381.80.040100.012.5['rain']0.00.044.621.3260.41005.084.512.787.77.45.010.007:19:38170385237816:36:3617038857960.59Rain, Partially cloudyPartly cloudy throughout the day with rain.rain['72505394728', '72055399999', 'KLGA', 'KJRB', 'F1417', 'KNYC', '72503014732']obs
90902023-12-3017039124007.93.96.06.60.33.30.266.50.0000.00.0NaN0.00.078.632.4270.21005.066.916.043.43.83.010.007:19:51170393879116:37:2217039722420.62Partially cloudyPartly cloudy throughout the day.partly-cloudy-day['72505394728', '72055399999', 'KLGA', 'KJRB', 'F1417', 'KNYC', '72503014732']obs
91912023-12-3117039988006.24.35.55.01.03.6-2.755.80.0000.00.0NaN0.00.038.918.2268.31014.896.716.031.92.62.010.007:20:02170402520216:38:0917040586890.65OvercastCloudy skies throughout the day.cloudy['72505394728', '72055399999', 'KLGA', 'KJRB', 'F1417', 'KNYC', '72503014732']obs